M&A Deal Sourcing – A Significant Opportunity for Machine Learning

It is somewhat easier to identify acquisition targets today than it was 10 years ago given the advent of private company databases such as Bob’s Guide, Crunchbase, CB Insights, PitchBook and others. However, we have yet to find a data service that combines a robust private company taxonomy with machine learning. The benefit of doing so would be to provide automated M&A target recommendations based upon user criteria. Corporate Development executives and I-Bankers would pay a pretty price for this service.

The M&A target company prospecting effort is almost entirely manual. Everything from discovery to information gathering. The previously mentioned data services perform what I describe as Level One information gathering where for a given company listed in the database service, users may find: Company Description (high-level), Company HQ Location, Website URL, Key Employees and whether or not the target company has executed a venture round(s).

Level Two information gathering is focused around capturing detailed information about a given company such as revenue, EBITDA, headcount and most importantly how exactly the company in question earns its living. What are the products it sells? What are the services it provides?

For example, a cloud services company may provide remote hosting services. Further, that company may offer embedded Artificial Intelligence, Machine Learning and more as part of its complete services offering. This descriptive information would need to be fleshed out at a detailed level and descriptive tags (“AI”, “ML”, “cloud”, “cybersecurity” “remote hosting”…) would need to be appended to each company’s description. Once these tags or data labels have been assigned, users could begin to build target company profiles – “Show me all “cloud” companies that provide “remote hosting” capability inclusive of embedded “cybersecurity” located within “North America” with minimum “12 month trailing revenue” of “$5 million”. Only then could the underlying machine learning layer begin to suggest target companies based on user defined criteria and pattern recognition. As new companies are added to the database service, users would receive automatic notification.

We acknowledge that the initial information gathering effort would be people intensive, but machine learning could help alleviate the manual effort by auto-populating known data fields, particularly in the case of companies that are close competitors.

One of the incumbents ought to provide this service or we will eventually get around to it at CEORater!